Process of Data Cleaning for Machine Learning

Data cleaning is one of the most important parts of machine learning. It plays an important role in building a machine learning model. Data quality is a significant aspect to train the ML model. Inaccurate data can have an impact on results. Data quality problems can occur anywhere in information systems.

A technique that helps to convert improper data into meaningful data. Machine Learning is data-driven. With the data cleaning techniques, your Machine Learning model will perform better. So, it is important to process data before use. Without quality data, it is unwise to expect a correct output.

Data cleaning refers to identifying and correcting errors in the dataset that may negatively impact a predictive model. It is used to refer to all kinds of tasks and activities to detect and repair errors in the data. These problems can be solved by using various data cleaning techniques. The process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. When combining multiple data sources, there are many opportunities for data to be duplicated or mislabeled.

Why do we need to clean our data?

Data cleaning is a key step before any form of analysis can be made on it.

At times, data needs to be cleaned/ preprocessed before we can extract useful information from it. Most real-life data have so many inconsistencies such as missing values, non-informative features, and so on, as such, there’s a constant need to clean our data before using it for us to get the best from it.

Datasets in pipelines are often collected in small groups and merged before being fed into a model. Merging multiple datasets means that redundancies and duplicates are formed in the data, which then need to be removed. Also, incorrect and poorly collected datasets can often lead to models learning incorrect representations of the data, thereby reducing their decision-making powers.

Data Cleaning fixes these major issues:

• Duplication
• Irrelevance
• Inaccuracy
• Inconsistency
• Missing data
• Lack of standardization
• Outliers

Significantly, different types of data will require different types of cleaning. Data cleansing involve a variety of cleaning steps:

Data Cleansing Steps:

Removing Unwanted Observations

This includes deleting duplicate/ redundant or irrelevant values from the dataset. Duplicate observations most frequently arise during data collection and Irrelevant comments don’t actually fit the specific problem you’re trying to solve.

Fixing Structural data

The errors that arise during measurement, transfer of data, or other similar situations are called structural errors. Structural errors include typos in the name of features, the same attribute with a different name, mislabeled classes, i.e. separate classes that should really be the same or inconsistent capitalization.

Managing unwanted outliers

Outliers can cause problems with certain types of models. For example, linear regression models are less robust to outliers than decision tree models. Generally, we should not remove outliers until we have a legitimate reason to remove them. Sometimes, removing them improves performance, sometimes not. So, one must have a good reason to remove the outlier, such as suspicious measurements that are unlikely to be part of real data.

Handling Missing Data

Missing data is a deceptively tricky issue in machine learning. We cannot just ignore or remove the missing observation. They must be handled carefully as they can be an indication of something important.

So, missing data is always informative and an indication of something important. And we must be aware of our algorithm of missing data by flagging it. By using this technique of flagging and filling, you are essentially allowing the algorithm to estimate the optimal constant for missingness, instead of just filling it in with the mean.

Benefits of data cleaning

Having clean data will ultimately increase overall productivity and allow for the highest quality information in your decision-making. Benefits include:

• Removal of errors when multiple sources of data are at play.
• Fewer errors make for happier clients and less-frustrated employees.
• Ability to map the different functions and what your data is intended to do.
• Monitoring errors and better reporting to see where errors are coming from, making it easier to fix incorrect or corrupt data for future applications.
• Using tools for data cleaning will make for more efficient business practices and quicker decision-making.

Wrapping It All Up

Data Cleaning is a critical process for the success of any machine learning function. For most machine learning projects, most effort is spent on data cleaning, but there are various other methods of refining your dataset and making your ML dataset error-free.

The main purpose of data cleaning machine learning is to find and remove errors along with any duplicate data, to build a reliable dataset. This increases the quality of the training data for analytics and facilitates decision-making. Four different steps in data cleaning to make the data more reliable and to produce good results. After properly completing the Data Cleaning steps, we will have a robust dataset that avoids many of the most common issues.

TagX provides Data Cleaning and preprocessing services to help enterprises develop custom solutions for face detection, vehicle detection, driver behavior detection, anomaly detection, and chatbots, running on machine learning algorithms.